If you want to know what the future of IT looks like, it's always good to look to IBM (News - Alert). The company pioneered and championed PCs, the Internet, open source software, Linux, APIs and AI, while shedding commodity products such as building PCs and producing chips. Today, the company is leaning hard into cloud with a data -centric vision different from Amazon, Google and Microsoft (News - Alert).
Data is the key for the cloud in the enterprise, says IBM, as opposed to the e-commerce roots of Amazon, the search and advertising basis for Google (News - Alert), or Microsoft's cloud-first/mobile-first strategy. Enterprises run on data, with an estimated 80 percent of the world's data NOT on the Web, but tucked away in private servers.
“We bring the ability to come to everything from the cloud,” said David Kenny, IBM Senior Vice President of Watson and Cloud Platform. “We have a strong public cloud and we are unique to connect to local clouds in data centers.”
IBM recognizes that while businesses are embracing the concept of using a public cloud, most have information they do not want to located outside of corporate boundaries for various reasons. The customer can mix and match positioning of data as needed, using IBM services and software to deliver a seamless framework between in-house and public clouds.
The “data” in the IBM story is in management and analytics. IBM executives boast their cloud strategy was built by and for data scientists – or as I like to think of it, the profession formerly known as statisticians. The data scientist uses mathematics, statistics, and various other disciplines to extract insights and knowledge from both structured and unstructured data. Applicable buzzwords for the data scientist include machine learning, classification, cluster analysis, data mining, and visualization, according to Wikipedia.
Having a strategy and tools to dig through data, aka Big Data, is the practice and domain of data scientists. The hottest tool in IBM's toolbox is artificial intelligence (AI), with Watson center stage. Watson can be accessed in a variety of ways, starting with APIs to call specific functions, including language translation, natural language understanding, personality insights, speech to text, text to speech, image recognition, and various forms of analytics. Developers can start with Watson APIs to build applications to pull information from unstructured data, tap into GitHub-based code for examples, and move onto building more elaborate programs.
Turn-key IBM Watson services represent the highest value, as they are ready to use, no programming required services where a Watson cognitive machine/age is already assembled and trained to work on a particular problem set. For example, IBM Watson Analytics provides a guided data analysis and automatic data visualization service that has the ability to quickly discover patterns and meaning in data. The tool uses natural language processing to enable a dialog between the user and Watson, with heavier analysis performed on the fly.
Vertical applications represent the pinnacle of Watson services, where the AI has been specifically trained to deal with a specific area of knowledge or skill by subject matter experts. Watson has been trained and is being used to focus on areas such as music preference discovery; regulatory compliance in the financial, engineering and energy industries; and assisting in physical and mental healthcare.
Cybersecurity is a prime example where IBM is “drinking its champagne.” IBM's security practice uses Watson today to quickly detect and define cyberattacks, reducing the time an enterprise security analyst would need to do the work from hours to minutes. Proactively, IBM has released the MaaS360 (News - Alert) Advisor, enabling enterprises to use machine learning to analyze devices on the corporate network. Once the data is gathered, the cognitive assistant provides recommended policies, patches, and customized best practices to better manage and protect those devices.
The key to IBM's data strategy is the synergistic nature of each part when all three are put together. The cloud is good, “Big Data” analysis benefits come from being able to look at everything quickly and easily stored on the cloud, and AI provides the tools to rapidly analyze and exploit that data. Developers and VARs alike need to start thinking about how to best leverage IBM's strategy for their own businesses.
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